11 citations · 13 across the 4 of their papers we have counts for
7 papers
AnoFel: Supporting Anonymity for Privacy-Preserving Federated Learning
Ghada Almashaqbeh, Zahra Ghodsi
Federated learning enables users to collaboratively train a machine learning model over their private datasets. Secure aggregation protocols are employed to mitigate information le…
Circa: Stochastic ReLUs for Private Deep Learning
Zahra Ghodsi, Nandan Kumar Jha, Brandon Reagen +1
The simultaneous rise of machine learning as a service and concerns over user privacy have increasingly motivated the need for private inference (PI). While recent work demonstrate…
Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles
Zahra Ghodsi, Siva Kumar Sastry Hari, Iuri Frosio +5
Extracting interesting scenarios from real-world data as well as generating failure cases is important for the development and testing of autonomous systems. We propose efficient m…
DeepReDuce: ReLU Reduction for Fast Private Inference
Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg +1
The recent rise of privacy concerns has led researchers to devise methods for private neural inference -- where inferences are made directly on encrypted data, never seeing inputs.…
Outsourcing Private Machine Learning via Lightweight Secure Arithmetic Computation
Siddharth Garg, Zahra Ghodsi, Carmit Hazay +3
In several settings of practical interest, two parties seek to collaboratively perform inference on their private data using a public machine learning model. For instance, several…
ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Neural Network Accelerators
Jeff Zhang, Kartheek Rangineni, Zahra Ghodsi +1
Hardware accelerators are being increasingly deployed to boost the performance and energy efficiency of deep neural network (DNN) inference. In this paper we propose Thundervolt, a…